VIDOSAT: High-dimensional sparsifying transform learning for online video denoising

Bihan Wen, Saiprasad Ravishankar, Yoram Bresler

Research output: Contribution to journalArticle

Abstract

Techniques exploiting the sparsity of images in a transform domain are effective for various applications in image and video processing. In particular, transform learning methods involve cheap computations and have been demonstrated to perform well in applications, such as image denoising and medical image reconstruction. Recently, we proposed methods for online learning of sparsifying transforms from streaming signals, which enjoy good convergence guarantees and involve lower computational costs than online synthesis dictionary learning. In this paper, we apply online transform learning to video denoising. We present a novel framework for online video denoising based on high-dimensional sparsifying transform learning for spatio-temporal patches. The patches are constructed either from corresponding 2D patches in successive frames or using an online block matching technique. The proposed online video denoising requires little memory and offers efficient processing. Numerical experiments evaluate the performance of the proposed video denoising algorithms on multiple video data sets. The proposed methods outperform several related and recent techniques, including denoising with 3D DCT, prior schemes based on dictionary learning, non-local means, background separation, and deep learning, as well as the popular VBM3D and VBM4D.

Original languageEnglish (US)
Article number8438535
Pages (from-to)1691-1704
Number of pages14
JournalIEEE Transactions on Image Processing
Volume28
Issue number4
DOIs
StatePublished - Apr 2019

Keywords

  • Sparse representations
  • big data
  • data-driven techniques
  • machine learning
  • online learning
  • sparsifying transforms
  • video denoising

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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